Factors influencing academic performance and dropout rates in higher education

Figures & data

Figure 1. A broad variety of factors potentially impacting students’ academic success (based on Alyahyan & Düştegör, 2020).

Figure 2. The process of the research method (based on page et al., 2021; S1).

Table 1. Research methods used in the filtered empirical publications (N = 128).

Type of research method	Frequency of research methods (%)
Correlation	20
Structural equation modelling (SEM, including path analysis)	16
Artificial neural networks (ANN)	13
Linear regression	10
Logistic regression	10
ANOVA	8
Decision tree (including random forest)	6
Descriptive statistics (only)	5
Miscellanea	12

Table 2. The predictive power of EDM algorithms.

Reference	Algorithms used	Factors considered	Sample size	Best algorithm and recall (%)
Ahmad and Shahzadi (2018)*	MLP	11	300	MLP − 93.20
Aulck et al. (2017)	LR RF, KNN	10	32,538	LR − 66.59
Bayer et al. (2012)	J48, IB1, PART, SMO, NB	30	775	PART − 92.30
Berens et al. (2019)**	LR, RF, ANN, AdaBoost	17	29,700	RF − 89.01
Díaz et al. (2021)***	C4.5, CART, BN, MLP, KNN, SVM	20	1,055	SVM − 94.6
Hamoud et al. (2018)	J48, random tree, REPtree	24	161	J48–62.10
Mueen et al. (2016)	NB, C4.5 MLP	32	87	NB − 86.30
Rodríguez-Hernández et al. (2021)	GLiM, LR, FLM, GBT, DT, RF, ANN	18	162,030	ANN − 81.50

Notes: ANN = artificial neural network, C4.5 = a decision tree algorithm, CART = a decision tree algorithm, DT = decision tree (not specified), FLM = Fast Large margin (a support vector machine algorithm), GliM = generalised linear model (a logistic regression model), GBT = gradient boost trees (a decision tree algorithm), IB1 = a nearest neighbours algorithm, J48 = a decision tree algorithm, KNN = k-nearest neighbours, LR = logistic regression, MLP = multilayer perceptron, NB = Naïve Bayes, PART = a rule learner algorithm, RF = random forest, SMO = a support vector machine algorithm. *only one method was used. **second semester data is considered. ***median of the five faculties.

Table 3. Frequency of the factors behind academic performance in the filtered empirical publications (N = 152).

Factor category	Factor description	Frequency (%)
Psychological	Big-Five personality characteristics, Coping strategies, Engagement/adjustment, Grit, Motivation, Problem-solving skills, Satisfaction, Self-efficacy, Self-regulated learning strategies, Stress	32
(Prior) academic achievement	High school background (high school results and type), Admissions test results University-data: semester GPA, CGPA, ECTS	31.5
Student demographics	Gender, Age, Race/ethnicity, Socioeconomic status, Parents’ education and occupation, Family size, Family income, Finances, Peers	31.5
Student’s environment	Type of programme/curriculum	4.5
Student e-learning activity	Number of logins, Number of tasks, Number of tests, Assessment activities, Number of discussion board entries, Number of accesses of the virtual classroom	0.5

Table 4. Percentage frequency distribution of factors behind academic performance in the filtered empirical publications (N = 153).

Factor category	Factor description	Frequency (%)	Correlation range	Path range
Psychological	(intrinsic) Motivation	15	.14–.36	.16–.16*
	Self-regulated learning strategies (SRLS)	12	.057–.44	.12–.53
	Self-efficacy	7	.17–.39	.15–.55
	Satisfaction	4	.26–.57	.32–.50
	Big-Five personality characteristics (only conscientiousness)	3	.24–.47	.41–.41*
	Engagement/adjustment	3	.31–.60	.18–.76
	Stress	3	.30–.30*	NI
	Problem-solving skills	1	NI	NI
	⅀	48
(Prior) academic achievement	Semester GPA, CGPA	12	.25–.28	.21–.49
	High school results (HSGPA) and type	8	.28–.62	.13–.35
	ECTS	7	.43–.59	.22–.22*
	Admissions test results	2	.25–.51	.48–.48*
	⅀	29
Student demographics	Family background (family income, family size, parents’ education and occupation, socioeconomic status)	9	.081–.56	.11–.72
	Gender	8	.27–.31	.11–.14
	Finances	4	.16–.16*	NI
	Age	2	.014–.51	.09–.17
	⅀	22
Student’s environment	Type of programme/curriculum	1	NI	NI
	⅀	1

Notes: NI = no information was found in the filtered publications; *Data is derived from only one publication.

Table 5. Correlation difference of factors behind academic performance in the filtered empirical publications.

Difference in correlation
Factor category	Small (Δ<.2)	Medium (.2<Δ<.4)	Large (.4<Δ)
Psychological		(intrinsic) Motivation, SRLS, Self-efficacy, Satisfaction, Conscientiousness, Engagement
(Prior) academic achievement	GPA/CGPA, ECTS	HSGPA, Admissions test results
Student demographics	Gender		Family background, Age

Table 6. A model of factors influencing academic performance based on the filtered publications.

Input: student factors	Throughput factors	Output: academic performance
Ability: problem-solving (skills)	Engagement	(C)GPA
Demographic factors: age, gender, family background	Work
Prior education: HSGPA, high school quality	Finances	ECTS
Personality: SRLS, intrinsic motivation, conscientiousness, self-efficacy

Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: Literature review and best practices. International Journal of Educational Technology in Higher Education, 17(3), 1–21. https://doi.org/10.1186/s41239-020-0177-7

 Web of Science ®Google Scholar

Ahmad, Z., & Shahzadi, E. (2018). Prediction of students’ academic performance using artificial neural network. Bulletin of Education and Research, 40(3), 157–164.

 Google Scholar

Aulck, L., Velagapudi, N., Blumenstock, J., & West, J. (2017). Predicting student dropout in higher education. ICML Workshop on #Data4Good: Machine Learning in Social Good Applications. https://doi.org/10.48550/arXiv.1606.06364

 Google Scholar

Bayer, J., Bydzovska, H., Geryk, J., Obsivac, T., & Popelinsky, L. (2012). Predicting dropout from social behaviour of students. In Y. Kalina, Z. Osmar, H. Arnon, Y. Michael, & S. John (Eds.) EDM 2012 Proceedings of the Fifth International Conference on Educational Data Mining, Chania, Greece. (pp. 103–109).

 Google Scholar

Berens, J., Schneider, K., Gortz, S., Oster, S., & Burghoff, J. (2019). Early detection of students at risk – predicting student dropouts using administrative Student data and machine learning methods. Journal of Educational Data Mining, 11(3), 1–41. https://doi.org/10.2139/ssrn.3275433

 Google Scholar

Díaz, I., Bernardo, A. B., Esteban, M., & J, R.-M. L. (2021). Variables influencing university dropout: A machine learning-based study. In Á. Herrero, C. Cambra, D. Urda, J. Sedano, H. Quintián, & E. Corchado (Eds.) The 11th International Conference on EUropean Transnational Educational (ICEUTE 2020). Advances in Intelligent Systems and Computing, 1266. (pp. 93–104). https://doi.org/10.1007/978-3-030-57799-5_10

 Google Scholar

Hamoud, A. K., Hashim, A. S., & Awadh, W. A. (2018). Predicting student performance in higher education institutions using decision tree analysis. International Journal of Interactive Multimedia and Artificial Intelligence, 5(2), 26–31. https://doi.org/10.9781/ijimai.2018.02.004

 Web of Science ®Google Scholar

Mueen, A., Zafar, B., & Manzoor, U. (2016). Modeling and predicting students’ academic performance using data mining techniques. International Journal of Modern Education and Computer Science, 11(11), 36–42. https://doi.org/10.5815/ijmecs.2016.11.05

 Google Scholar

Rodríguez-Hernández, C. F., Musso, M., Kyndt, E., & Cascallar, E. (2021). Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation. Computers and Education: Artificial Intelligence, 2, 100018. https://doi.org/10.1016/j.caeai.2021.100018

 Google Scholar